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An Artificial Intelligent Signal Amplification System for in vivo Detection of miRNA

  • Xibo Ma*
  • , Lei Chen
  • , Yingcheng Yang
  • , Weiqi Zhang
  • , Peixia Wang
  • , Kun Zhang
  • , Bo Zheng
  • , Lin Zhu
  • , Zheng Sun
  • , Shuai Zhang
  • , Yingkun Guo
  • , Minmin Liang
  • , Hongyang Wang
  • , Jie Tian
  • *Corresponding author for this work
  • CAS - Institute of Automation
  • School of Artificial Intelligence
  • University of Chinese Academy of Sciences
  • International Co-operation Laboratory on Signal Transduction
  • Naval Medical University
  • National Laboratory of Biomacromolecules
  • Department of Radiology
  • West China Second University Hospital
  • Beijing Advanced Innovation Center for Big Data-Based Precision Medicine
  • Beihang University

Research output: Contribution to journal › Article › peer-review

Abstract

MicroRNAs (miRNA) have been identified as oncogenic drivers and tumor suppressors in every major cancer type. In this work, we design an artificial intelligent signal amplification (AISA) system including double-stranded SQ (S, signal strand; Q, quencher strand) and FP (F, fuel strand; P, protect strand) according to thermodynamics principle for sensitive detection of miRNA in vitro and in vivo. In this AISA system for miRNA detection, strand S carries a quenched imaging marker inside the SQ. Target miRNA is constantly replaced by a reaction intermediate and circulatively participates in the reaction, similar to enzyme. Therefore, abundant fluorescent substances from S and SP are dissociated from excessive SQ for in vitro and in vivo visualization. The versatility and feasibility for disease diagnosis using this system were demonstrated by constructing two types of AISA system to detect Hsa-miR-484 and Hsa-miR-100, respectively. The minimum target concentration detected by the system in vitro (10 min after mixing) was 1/10th that of the control group. The precancerous lesions of liver cancer were diagnosed, and the detection accuracy were larger than 94% both in terms of location and concentration. The ability to establish this design framework for AISA system with high specificity provides a new way to monitor tumor progression and to assess therapeutic responses.

Original languageEnglish
Article number330
JournalFrontiers in Bioengineering and Biotechnology
Volume7
DOIs
Publication statusPublished - 21 Nov 2019

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • an artificial intelligent signal amplification system
  • early diagnosis of precancerous lesions
  • fluorescent molecular tomography
  • in vivo detection of non-coding RNA
  • stem cell tracing

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